Results Notebook

1 Overview

1.1 Data inspection

1.2 Participants

A total of 1146 participants were recruited through a survey posted on Prolific. 184 were excluded as they did not complete the survey, and 98 were excluded as they are vegan/vegetarian, and 5 were excluded for indicating that their results should not be included in the analysis. 13 were excluded for failing to select the correct response in an attention check. The final sample (N = 846) ranged in age 18 to 79 (Mdn_age = 34.50, M_age = 37.21, SD = 13.58). The participants were predominantly female (56.38%). The participants received £0.35 ($0.45) for successfully completing the task.

1.3 Randomization check

A preliminary randomization check was conducted. The check revealed no systematic differences between the three conditions in gender, age, political position, and nationality (all p’s > .05).

Table 1.1: Randomisation check

Item	Dynamic	Static	No norm	Significance test
Age (years)	37.34 \(\pm\) 14.22	37.90 \(\pm\) 12.97	36.40 \(\pm\) 13.55	\(F(2, 843) = 0.89\), \(\mathit{MSE} = 184.49\), \(p = .409\)
Gender (%)	Male (39.49%) Female (60.14%) Other (0.36%)	Male (41.9%) Female (58.1%)	Male (48.25%) Female (51.05%) Other (0.7%)	\(\chi^2(4, n = 846) = 6.92\), \(p = .140\)
Political position	3.47 \(\pm\) 1.22	3.54 \(\pm\) 1.26	3.45 \(\pm\) 1.26	\(F(2, 843) = 0.39\), \(\mathit{MSE} = 1.55\), \(p = .679\)
Nationality (%)	1 (84.06%) 2 (3.62%) 3 (9.78%) 4 (0.72%) NA (1.81%)	1 (80.99%) 2 (5.28%) 3 (8.45%) 4 (1.41%) NA (3.87%)	1 (80.77%) 2 (5.94%) 3 (8.74%) 4 (1.75%) NA (2.8%)	\(\chi^2(6, n = 846) = 3.23\), \(p = .779\)

1.4 Correlations

Table 1.2: Means, Standard Deviations, Reliabilities, and Inter-Correlations Among Study Measures

	Alpha	M	SD	1	2	3	4	5	6
Interest		3.58	1.82
Attitude	0.90	4.64	1.29	.80**
Intention	0.98	4.22	1.80	.83**	.82**
Expectation	0.99	3.93	1.75	.80**	.80**	.92**
Intent/expectation composite		4.08	1.74	.83**	.83**	.98**	.98**
Perception of change		5.14	0.90	.27**	.26**	.27**	.25**	.27**
Preconformity		4.18	1.20	.44**	.40**	.39**	.37**	.39**	.37**

2 Confirmatory analyses

2.1 Does making dynamic norms about reduced meat consumption in the UK salient lead to higher interest in reducing meat consumption (compared to static norm salience)?

Sparkman and Walton (2017) found effects of dynamic norms on interest in reducing meat consumption ranging from M_diff = 0.60 – 0.78. Thus, the rough mean difference between dynamic and static norms expected in the sample is 0.69 on a 7 point Likert scale. Thus, I modeled H1 as a half-normal with an SD of 0.69. The plausible maximum effect was set at 1.38.

Figure 2.1: World cloud of participants response to text

Table 2.1:

Most frequent words in text

word	freq
health	216.00
environment	193.00
animals	144.00
climate	67.00
awareness	64.00
concerns	64.00
impact	56.00
media	50.00
vegan	43.00
better	35.00

The mean interest for participants in the dynamic norm condition was M = 3.64 (SD = 1.83), and the mean interest in the static norm condition was M = 3.68 (SD = 1.84). The mean interest in the no norm condition was M = 3.41 (SD = 1.77).

There was no difference in interest in reducing meat consumption between the dynamic norm (M = 3.64, SD = 1.83) and static norm (M = 3.68, SD = 1.84) conditions, \(\Delta M = -0.03\), 95% CI \([-0.34\), \(0.27]\), \(t(843) = -0.23\), \(p = .821\), d = -0.02, \(B_{\text{HN}(0, 0.69)}\) = 0.18, RR[0.65, 2].

Participants in the no-norm control condition showed the least interest in reducing meat consumption (M = 3.41, SD = 1.77) and did not differ from those in the dynamic-norm condition \(\Delta M = 0.23\), 95% CI \([-0.07\), \(0.53]\), \(t(843) = 1.52\), \(p = .130\), d = 0.13, or the static-norm condition \(\Delta M = 0.27\), 95% CI \([-0.03\), \(0.57]\), \(t(843) = 1.76\), \(p = .080\), d = 0.15. There was also no difference between the dynamic-norm condition and a combination of the control and static-norm conditions \(\Delta M = 0.10\), 95% CI \([-0.16\), \(0.36]\), \(t(843) = 0.74\), \(p = .458\).

Table 2.2:

Meat consumption by condition contrasts

	contrast	\(\Delta M\)	95% CI	\(t(843)\)	\(p\)
DYST	Dynamic, static	-0.03	\([-0.34\), \(0.27]\)	-0.23	.821
DYNO	Dynamic, control	0.23	\([-0.07\), \(0.53]\)	1.52	.130
STNO	Static, control	0.27	\([-0.03\), \(0.57]\)	1.76	.080
DYCONT	Dynamic, both	0.10	\([-0.16\), \(0.36]\)	0.74	.458
EXPNO	Norms, control	-0.25	\([-0.51\), \(0.01]\)	-1.89	.059

2.2 Will participants in the dynamic norm condition be more likely (than those in the static norm control) to predict a future decrease in meat consumption in the UK?

I modeled H2 using a half-normal distribution with a mean of 0 and SD of M_diff = 0.40. The plausible maximum effect was set at twice the predicted effect of M_diff = 0.80. A Bayes factor was calculated for each test.

Table 2.3: Expectations of future meat consumption

		Future Norm		Preconformity		Combined
Condition	\(n\)	\(M\)	\(SD\)	\(M\)	\(SD\)	\(M\)	\(SD\)
Dynamic	276	5.26	0.93	4.35	1.18	4.81	0.88
Static	284	5.20	0.85	4.24	1.19	4.72	0.85
No norm	286	4.97	0.89	3.95	1.18	4.46	0.84

2.2.0.1 Measure of perception of change: “In the next 5 years, I expect meat consumption in the UK to…”

There was no evidence one way or another for an effect of dynamic norm condition on expectations about future meat consumption, \(\Delta M = 0.06\), 95% CI \([-0.08\), \(0.21]\), \(t(843) = 0.85\), \(p = .397\), d = 0.07, \(B_{\text{HN}(0, 0.40)}\) = 0.42, RR[0.05, 0.8]

Table 2.4: Perception change contrasts

	contrast	estimate	ci	statistic	p.value
DYST	Dynamic, static	0.06	\([-0.08\), \(0.21]\)	0.85	.397
DYNO	Dynamic, control	0.30	\([0.15\), \(0.44]\)	3.94	< .001
STNO	Static, control	0.23	\([0.09\), \(0.38]\)	3.11	.002
DYCONT	Dynamic, both	0.18	\([0.05\), \(0.31]\)	2.75	.006
EXPNO	Norms, control	-0.26	\([-0.39\), \(-0.14]\)	-4.08	< .001

2.2.0.2 Measure of preconformity: “In the foreseeable future, to what extent do you think that many people will make an effort to eat less meat?”

There was no evidence one way or the other for there being a difference in anticipation that many people would make an effort to reduce their meat consumption in the future between the dynamic norm (M = 4.35, SD = 1.18) and static norm (M = 4.24, SD = 1.19) conditions, \(\Delta M = 0.11\), 95% CI \([-0.09\), \(0.31]\), \(t(843) = 1.08\), \(p = .279\), d = 0.09, \(B_{\text{HN}(0, 0.40)}\) = 0.72, RR[0.05, 1.5].

Table 2.5: Preconformity contrasts

	contrast	estimate	ci	statistic	p.value
DYST	Dynamic, static	0.11	\([-0.09\), \(0.31]\)	1.08	.279
DYNO	Dynamic, control	0.40	\([0.20\), \(0.60]\)	4.00	< .001
STNO	Static, control	0.29	\([0.10\), \(0.49]\)	2.94	.003
DYCONT	Dynamic, both	0.25	\([0.08\), \(0.43]\)	2.93	.004
EXPNO	Norms, control	-0.35	\([-0.52\), \(-0.18]\)	-4.02	< .001

2.2.0.3 Combined

There was no evidence one way or the other for there being a difference in anticipation that many people would make an effort to reduce their meat consumption in the future between the dynamic norm (M = 4.81, SD = 0.88) and static norm (M = 4.72, SD = 0.85) conditions, \(\Delta M = 0.09\), 95% CI \([-0.06\), \(0.23]\), \(t(843) = 1.19\), \(p = .235\), d = 0.10, \(B_{\text{HN}(0, 0.40)}\) = 0.62, RR[0.05, 1.25].

Table 2.6: Combined contrasts

	contrast	estimate	ci	statistic	p.value
DYST	Dynamic, static	0.09	\([-0.06\), \(0.23]\)	1.19	.235
DYNO	Dynamic, control	0.35	\([0.21\), \(0.49]\)	4.81	< .001
STNO	Static, control	0.26	\([0.12\), \(0.40]\)	3.65	< .001
DYCONT	Dynamic, both	0.22	\([0.09\), \(0.34]\)	3.45	.001
EXPNO	Norms, control	-0.31	\([-0.43\), \(-0.18]\)	-4.89	< .001

3 Secondary analyses

3.1 Will there be a difference in perceptions of current static norm across the dynamic and static norm conditions?

The SESOI for percentage difference is ± 5%. The SESOI for mean difference on the Likert scale is ± 0.5.

TOST results: t-value lower bound: 129.959 p-value lower bound: 0e+00 t-value upper bound: \(-23.608\) p-value upper bound: \(2.99\times10^{-86}\) degrees of freedom : 557.66

Equivalence bounds (raw scores): low eqbound: \(-5\) high eqbound: 5

TOST confidence interval: lower bound 90% CI: 3.355 upper bound 90% CI: 3.57

NHST confidence interval: lower bound 95% CI: 3.335 upper bound 95% CI: 3.591

Equivalence Test Result: The equivalence test was significant, t(557.66) = \(-23.608\), p = \(2.99\times10^{-86}\), given equivalence bounds of \(-5\) and 5 (on a raw scale) and an alpha of 0.05. Null Hypothesis Test Result: The null hypothesis test was significant, t(557.66) = 53.175, p = \(1.51\times10^{-220}\), given an alpha of 0.05. Based on the equivalence test and the null-hypothesis test combined, we can conclude that the observed effect is statistically different from zero and statistically equivalent to zero.

3.2 Will there be a difference in how meat consumption is construed across the dynamic and static norm conditions?

The SESOI for difference in number of meals is ± 2 meals.

TOST results: t-value lower bound: 15.086 p-value lower bound: \(1.12\times10^{-43}\) t-value upper bound: \(-13.542\) p-value upper bound: \(1.25\times10^{-36}\) degrees of freedom : 557.55

Equivalence bounds (raw scores): low eqbound: \(-5\) high eqbound: 5

TOST confidence interval: lower bound 90% CI: \(-0.306\) upper bound 90% CI: 0.845

NHST confidence interval: lower bound 95% CI: \(-0.417\) upper bound 95% CI: 0.956

Equivalence Test Result: The equivalence test was significant, t(557.55) = \(-13.542\), p = \(1.25\times10^{-36}\), given equivalence bounds of \(-5\) and 5 (on a raw scale) and an alpha of 0.05. Null Hypothesis Test Result: The null hypothesis test was non-significant, t(557.55) = 0.772, p = .441, given an alpha of 0.05. Based on the equivalence test and the null-hypothesis test combined, we can conclude that the observed effect is statistically not different from zero and statistically equivalent to zero.

4 Exploratory analyses

4.1 Does dynamic norm (versus static norm) information lead to more positive attitudes, intentions, and expectations to reduce meat consumption?

4.1.1 Trace plots

Figure 4.1: Traceplots of regression parameters

Figure 4.2: Traceplots of regression parameters

Figure 4.3: Traceplots of regression parameters

4.1.2 Posterior plots

Figure 4.4: Posterior uncertainty intervals

Figure 4.5: Posterior density plot

4.1.3 Summary table

Table 4.1: Posterior results for simple model (H3)

	Model 1: Uninformative priors\(^a\)					Model 2: Weakly informative priors\(^b\)						Model 3: Moderately informative priors\(^c\)
	Mean (SD)	95% PPI	neff	PSRF	Prior	Mean (SD)	95% PPI	neff	PSRF	Bias (%)	Prior	Mean (SD)	95% PPI	neff	PSRF	Bias (%)	Prior
Interest	0.040 (0.156)	-0.265, 0.349	819.155	1.003	normal(0,10)	0.089 (0.154)	-0.212, 0.388	771.276	1.005	122.5	normal(0.5,0.75)	0.196 (0.132)	-0.059, 0.459	889.294	1.004	390	normal(0.5, 0.35)
Attitude	-0.049 (0.111)	-0.267, 0.165	771.073	1.004	normal(0,10)	-0.012 (0.112)	-0.234, 0.207	730.007	1.005	-75.51	normal(0.5,0.75)	0.064 (0.093)	-0.117, 0.244	928.977	1.003	-230.61	normal(0.5, 0.35)
Intention/Expectation	-0.036 (0.144)	-0.314, 0.251	808.662	1.005	normal(0,10)	0.011 (0.145)	-0.271, 0.291	725.346	1.006	-130.56	normal(0.5,0.75)	0.113 (0.122)	-0.12, 0.353	931.226	1.004	-413.89	normal(0.5, 0.35)
Note. PPI = posterior probability interval; PSRF = potential scale reduction factor; neff = effective sample size
a ppp = .499 b ppp = .493 c ppp = .452

4.2 Does age interact with norm condition to influence dependent variables?

4.2.1 Trace plots

Figure 4.6: Traceplots of regression parameters

Figure 4.7: Traceplots of regression parameters

Figure 4.8: Traceplots of regression parameters

4.2.2 Posterior plots

Figure 4.9: Posterior uncertainty intervals

4.2.3 Summary table

Table 4.2: Posterior results for multi-sample analysis by age (H4)

	Model 1: Uninformative priors\(^a\)					Model 2: Informative priors\(^b\)						Model 3: Informative priors\(^c\)
Parameter	Mean (SD)	95% PPI	neff	PSRF	Prior	Mean (SD)	95% PPI	neff	PSRF	Bias (%)	Prior	Mean (SD)	95% PPI	neff	PSRF	Bias (%)	Prior
Young adults
Interest	0.233 (0.330)	-0.407, 0.835	1026.013	1	normal(0,10)	0.369 (0.279)	-0.146, 0.927	1167.716	1.003	58.37	normal(0.5,0.75)	0.512 (0.214)	0.076, 0.928	1199.913	1	119.74	normal(0.5,0.35)
Attitude	0.090 (0.240)	-0.39, 0.548	977.768	1.001	normal(0,10)	0.190 (0.207)	-0.203, 0.608	1009.094	1.004	111.11	normal(0.5,0.75)	0.320 (0.165)	0.015, 0.662	1227.428	1	255.56	normal(0.5,0.35)
Intention/Expectation	-0.099 (0.303)	-0.7, 0.464	946.394	1.001	normal(0,10)	0.038 (0.262)	-0.448, 0.565	1067.330	1.003	-138.38	normal(0.5,0.75)	0.214 (0.203)	-0.18, 0.624	1245.482	1	-316.16	normal(0.5,0.35)
Middle-aged adults
Interest	0.071 (0.230)	-0.381, 0.512	950.455	1.004	normal(0,10)	0.153 (0.205)	-0.26, 0.557	1241.663	0.999	115.49	normal(0.5,0.75)	0.322 (0.163)	0, 0.639	1274.756	1	353.52	normal(0.5,0.35)
Attitude	-0.057 (0.169)	-0.381, 0.277	1025.134	1.004	normal(0,10)	0.001 (0.151)	-0.289, 0.298	1307.024	0.999	-101.75	normal(0.5,0.75)	0.129 (0.127)	-0.122, 0.384	1272.112	1.001	-326.32	normal(0.5,0.35)
Intention/Expectation	0.026 (0.224)	-0.404, 0.466	958.833	1.004	normal(0,10)	0.103 (0.199)	-0.273, 0.498	1223.354	1	296.15	normal(0.5,0.75)	0.271 (0.161)	-0.05, 0.597	1294.632	1	942.31	normal(0.5,0.35)
Old adults
Interest	-0.154 (0.274)	-0.684, 0.391	1307.916	1.002	normal(0,10)	-0.007 (0.255)	-0.488, 0.52	1039.340	1.001	-95.45	normal(0.5,0.75)	0.240 (0.196)	-0.135, 0.632	1124.467	1.003	-255.84	normal(0.5,0.35)
Attitude	-0.121 (0.181)	-0.488, 0.232	1340.006	1.001	normal(0,10)	-0.023 (0.175)	-0.362, 0.32	1081.721	1.002	-80.99	normal(0.5,0.75)	0.142 (0.137)	-0.133, 0.413	1221.826	1.002	-217.36	normal(0.5,0.35)
Intention/Expectation	-0.064 (0.253)	-0.562, 0.437	1331.828	1.002	normal(0,10)	0.065 (0.239)	-0.395, 0.519	1090.644	1	-201.56	normal(0.5,0.75)	0.283 (0.183)	-0.069, 0.649	1138.656	1.002	-542.19	normal(0.5,0.35)
Note. PPI = posterior probability interval; PSRF = potential scale reduction factor; neff = effective sample size
a ppp = .486 b ppp = .485 c ppp = .388

Table 4.3: Posterior results for moderation model using age as continuous variable (H4)

	Estimate	Post.SD	pi.lower	pi.upper	Rhat	neff	Prior
Interest
conditionbi	0.053	0.157	-0.25	0.367	1.000	842.822	normal(0,10)
age_cent	-0.017	0.008	-0.033	0	1.001	941.738	normal(0,10)
condition_age	0.012	0.012	-0.01	0.035	1.000	956.013	normal(0,10)
Attitudes
conditionbi	-0.042	0.113	-0.266	0.182	1.000	828.461	normal(0,10)
age_cent	-0.008	0.006	-0.02	0.004	1.001	939.182	normal(0,10)
condition_age	0.006	0.008	-0.01	0.022	1.001	916.168	normal(0,10)
Intentions/Expectations
conditionbi	-0.038	0.149	-0.332	0.264	1.001	831.300	normal(0,10)
age_cent	0.001	0.008	-0.014	0.017	1.001	881.743	normal(0,10)
condition_age	-0.001	0.011	-0.023	0.02	1.000	897.894	normal(0,10)

4.3 Do demographic factors such as age, gender, and political position predict the primary dependent variables?

4.3.1 Trace plots

Figure 4.10: Traceplots for estimated regression parameters

Figure 4.11: Traceplots for estimated regression parameters

Figure 4.12: Traceplots for estimated regression parameters

4.3.2 Posterior plots

Figure 4.13: Posterior uncertainty intervals

Figure 4.14: Posterior density plot

4.3.3 Summary table

Table 4.4: Posterior results for full model (H5)

	Model 1: Uninformative priors\(^a\)					Model 2: Informative priors\(^b\)						Model 3: Informative priors\(^c\)
Parameter	Mean (SD)	95% PPI	neff	PSRF	Prior	Mean (SD)	95% PPI	neff	PSRF	Bias (%)	Prior	Mean (SD)	95% PPI	neff	PSRF	Bias (%)	Prior
Interest
Condition	0.063 (0.150)	-0.232, 0.364	1639.072	1	normal(0,10)	0.105 (0.147)	-0.194, 0.381	1535.778	0.999	66.67	normal(0.5,0.75)	0.216 (0.129)	-0.048, 0.468	1416.422	1	242.86	normal(0.5,0.35)
Age	-0.002 (0.006)	-0.013, 0.009	2376.922	1	normal(0,10)	-0.003 (0.006)	-0.014, 0.009	2791.596	1	50	normal(0,10)	-0.003 (0.006)	-0.013, 0.008	2269.230	1	50	normal(0,10)
Gender	0.527 (0.154)	0.232, 0.831	1447.178	1.002	normal(0,10)	0.532 (0.157)	0.225, 0.841	1412.359	1.001	0.95	normal(0,10)	0.533 (0.150)	0.241, 0.817	1537.473	1.002	1.14	normal(0,10)
Politics	-0.311 (0.060)	-0.428, -0.196	1488.474	1.001	normal(0,10)	-0.309 (0.062)	-0.43, -0.186	1213.321	1	-0.64	normal(0,10)	-0.309 (0.062)	-0.432, -0.186	1365.964	1.001	-0.64	normal(0,10)
Attitudes
Condition	-0.032 (0.107)	-0.236, 0.183	1607.415	1.001	normal(0,10)	-0.003 (0.106)	-0.219, 0.199	1565.899	1	-90.62	normal(0.5,0.75)	0.075 (0.095)	-0.111, 0.265	1451.491	1.001	-334.38	normal(0.5,0.35)
Age	0.001 (0.004)	-0.007, 0.009	2319.560	1	normal(0,10)	0.001 (0.004)	-0.007, 0.009	2684.022	1	0	normal(0,10)	0.001 (0.004)	-0.007, 0.009	2628.132	1	0	normal(0,10)
Gender	0.298 (0.111)	0.089, 0.516	1303.598	1.003	normal(0,10)	0.303 (0.111)	0.09, 0.518	1316.893	1.001	1.68	normal(0,10)	0.304 (0.110)	0.091, 0.522	1500.929	1.002	2.01	normal(0,10)
Politics	-0.244 (0.044)	-0.331, -0.16	1436.536	1.002	normal(0,10)	-0.244 (0.045)	-0.331, -0.156	1290.656	1	0	normal(0,10)	-0.242 (0.044)	-0.328, -0.153	1268.313	1.001	-0.82	normal(0,10)
Intention/Expectations
Condition	-0.019 (0.144)	-0.308, 0.263	1598.190	1	normal(0,10)	0.021 (0.138)	-0.259, 0.287	1507.211	1	-210.53	normal(0.5,0.75)	0.128 (0.122)	-0.113, 0.373	1389.121	1.001	-773.68	normal(0.5,0.35)
Age	0.008 (0.005)	-0.003, 0.018	2326.488	1	normal(0,10)	0.008 (0.005)	-0.003, 0.018	2690.547	0.999	0	normal(0,10)	0.008 (0.005)	-0.002, 0.018	2473.919	0.999	0	normal(0,10)
Gender	0.592 (0.148)	0.308, 0.89	1346.050	1.003	normal(0,10)	0.598 (0.148)	0.304, 0.89	1366.947	1	1.01	normal(0,10)	0.599 (0.146)	0.308, 0.881	1581.186	1.002	1.18	normal(0,10)
Politics	-0.275 (0.057)	-0.385, -0.165	1389.771	1.002	normal(0,10)	-0.274 (0.060)	-0.389, -0.156	1237.262	1	-0.36	normal(0,10)	-0.273 (0.059)	-0.39, -0.155	1313.996	1.001	-0.73	normal(0,10)
Note. PPI = posterior probability interval; PSRF = potential scale reduction factor; neff = effective sample size
a ppp = .507 b ppp = .508 c ppp = .476

5 Unregistered analyses

5.1 Power test

Sparkman and Walton (2017) found standardized effects of dynamic norms on interest in reducing meat consumption ranging from \(d\) = 0.31 – 0.41. To detect the average effect of \(d\) = 0.36, we would need 122 participants in each condition.

 Two-sample t test power calculation 

          n = 122.0922
          d = 0.36
  sig.level = 0.05
      power = 0.8
alternative = two.sided

NOTE: n is number in each group

5.2 Moderation of demographic variables

Table 5.1:

Exploring the effect moderation of demographic variables on attitudes

Predictor	\(b\)	95% CI	\(t(553)\)	\(p\)
Intercept	5.46	\([4.95\), \(5.97]\)	21.17	< .001
ConditionbiStatic	-0.18	\([-0.89\), \(0.52]\)	-0.51	.613
GenderbiFemale	0.35	\([0.04\), \(0.66]\)	2.21	.027
POLITICS	-0.27	\([-0.40\), \(-0.15]\)	-4.32	< .001
ConditionbiStatic \(\times\) GenderbiFemale	-0.10	\([-0.53\), \(0.33]\)	-0.47	.642
ConditionbiStatic \(\times\) POLITICS	0.06	\([-0.11\), \(0.23]\)	0.67	.505

Table 5.2:

Exploring the effect moderation of demographic variables on intention

Predictor	\(b\)	95% CI	\(t(553)\)	\(p\)
Intercept	4.78	\([4.11\), \(5.45]\)	14.02	< .001
ConditionbiStatic	-0.18	\([-1.12\), \(0.75]\)	-0.39	.697
GenderbiFemale	0.78	\([0.37\), \(1.19]\)	3.77	< .001
POLITICS	-0.32	\([-0.49\), \(-0.16]\)	-3.85	< .001
ConditionbiStatic \(\times\) GenderbiFemale	-0.41	\([-0.98\), \(0.16]\)	-1.41	.159
ConditionbiStatic \(\times\) POLITICS	0.12	\([-0.11\), \(0.34]\)	1.00	.317

Table 5.3:

Exploring the effect moderation of demographic variables on interest

predictor	\(b\)	95% CI	\(t(553)\)	\(p\)
Intercept	4.61	\([3.90\), \(5.31]\)	12.80	< .001
Condition	-0.27	\([-1.25\), \(0.72]\)	-0.53	.594
Gender	0.53	\([0.10\), \(0.96]\)	2.43	.015
Political position	-0.37	\([-0.54\), \(-0.19]\)	-4.14	< .001
Condition \(\times\) Gender	-0.01	\([-0.61\), \(0.59]\)	-0.03	.976
Condition \(\times\) Political position	0.09	\([-0.15\), \(0.33]\)	0.77	.443

5.3 Mediation

Table 5.4: Exploring mediating effect of attitude on intentions/expectations

Parameter	\(M\)	\(SD\)	Lower PPI	Upper PPI	Rhat	Prior
Direct effects
Interest ~ Condition	0.033	0.157	-0.272	0.35	1.004	normal(0,10)
Attitude ~ Condition	-0.051	0.112	-0.272	0.161	0.999	normal(0,10)
Intent/Expectation ~ Condition	-0.009	0.097	-0.196	0.192	1.004	normal(0,10)
Intent/Expectation ~ Attitude	0.625	0.048	0.531	0.721	1.000	normal(0,10)
Mediation
Indirect effects/nCondition > Attitude > Intention/Expectation	-0.032	0.070	-0.169	0.106	NA
Total effect	-0.040	0.120	-0.275	0.195	NA

6 Environment and data

6.1 Session information

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 4.1.0 (2021-05-18)
##  os       macOS Big Sur 10.16         
##  system   x86_64, darwin17.0          
##  ui       X11                         
##  language (EN)                        
##  collate  en_GB.UTF-8                 
##  ctype    en_GB.UTF-8                 
##  tz       Europe/London               
##  date     2021-06-29                  
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package      * version    date       lib source                        
##  abind          1.4-5      2016-07-21 [1] CRAN (R 4.1.0)                
##  assertthat     0.2.1      2019-03-21 [1] CRAN (R 4.1.0)                
##  backports      1.2.1      2020-12-09 [1] CRAN (R 4.1.0)                
##  bayesplot      1.8.1      2021-06-14 [1] CRAN (R 4.1.0)                
##  bfrr         * 0.0.0.9000 2021-06-29 [1] Github (debruine/bfrr@9b80a99)
##  bitops         1.0-7      2021-04-24 [1] CRAN (R 4.1.0)                
##  bookdown       0.22       2021-04-22 [1] CRAN (R 4.1.0)                
##  boot           1.3-28     2021-05-03 [1] CRAN (R 4.1.0)                
##  broom          0.7.8      2021-06-24 [1] CRAN (R 4.1.0)                
##  bslib          0.2.5.1    2021-05-18 [1] CRAN (R 4.1.0)                
##  cachem         1.0.5      2021-05-15 [1] CRAN (R 4.1.0)                
##  callr          3.7.0      2021-04-20 [1] CRAN (R 4.1.0)                
##  car            3.0-11     2021-06-27 [1] CRAN (R 4.1.0)                
##  carData        3.0-4      2020-05-22 [1] CRAN (R 4.1.0)                
##  cellranger     1.1.0      2016-07-27 [1] CRAN (R 4.1.0)                
##  cli            2.5.0      2021-04-26 [1] CRAN (R 4.1.0)                
##  coda           0.19-4     2020-09-30 [1] CRAN (R 4.1.0)                
##  codebook     * 0.9.2      2020-06-06 [1] CRAN (R 4.1.0)                
##  colorspace     2.0-2      2021-06-24 [1] CRAN (R 4.1.0)                
##  crayon         1.4.1      2021-02-08 [1] CRAN (R 4.1.0)                
##  curl           4.3.2      2021-06-23 [1] CRAN (R 4.1.0)                
##  data.table     1.14.0     2021-02-21 [1] CRAN (R 4.1.0)                
##  DBI            1.1.1      2021-01-15 [1] CRAN (R 4.1.0)                
##  dbplyr         2.1.1      2021-04-06 [1] CRAN (R 4.1.0)                
##  desc           1.3.0      2021-03-05 [1] CRAN (R 4.1.0)                
##  devtools       2.4.2      2021-06-07 [1] CRAN (R 4.1.0)                
##  digest         0.6.27     2020-10-24 [1] CRAN (R 4.1.0)                
##  dplyr        * 1.0.7      2021-06-18 [1] CRAN (R 4.1.0)                
##  ellipsis       0.3.2      2021-04-29 [1] CRAN (R 4.1.0)                
##  emmeans      * 1.6.1      2021-06-01 [1] CRAN (R 4.1.0)                
##  estimability   1.3        2018-02-11 [1] CRAN (R 4.1.0)                
##  evaluate       0.14       2019-05-28 [1] CRAN (R 4.1.0)                
##  ez             4.4-0      2016-11-02 [1] CRAN (R 4.1.0)                
##  fansi          0.5.0      2021-05-25 [1] CRAN (R 4.1.0)                
##  farver         2.1.0      2021-02-28 [1] CRAN (R 4.1.0)                
##  fastmap        1.1.0      2021-01-25 [1] CRAN (R 4.1.0)                
##  forcats      * 0.5.1      2021-01-27 [1] CRAN (R 4.1.0)                
##  foreign        0.8-81     2020-12-22 [1] CRAN (R 4.1.0)                
##  fs             1.5.0      2020-07-31 [1] CRAN (R 4.1.0)                
##  generics       0.1.0      2020-10-31 [1] CRAN (R 4.1.0)                
##  ggplot2      * 3.3.5      2021-06-25 [1] CRAN (R 4.1.0)                
##  ggridges       0.5.3      2021-01-08 [1] CRAN (R 4.1.0)                
##  glue           1.4.2      2020-08-27 [1] CRAN (R 4.1.0)                
##  gridExtra      2.3        2017-09-09 [1] CRAN (R 4.1.0)                
##  gtable         0.3.0      2019-03-25 [1] CRAN (R 4.1.0)                
##  haven          2.4.1      2021-04-23 [1] CRAN (R 4.1.0)                
##  here         * 1.0.1      2020-12-13 [1] CRAN (R 4.1.0)                
##  highr          0.9        2021-04-16 [1] CRAN (R 4.1.0)                
##  hms            1.1.0      2021-05-17 [1] CRAN (R 4.1.0)                
##  htmltools      0.5.1.1    2021-01-22 [1] CRAN (R 4.1.0)                
##  httr           1.4.2      2020-07-20 [1] CRAN (R 4.1.0)                
##  insight        0.14.2     2021-06-22 [1] CRAN (R 4.1.0)                
##  jquerylib      0.1.4      2021-04-26 [1] CRAN (R 4.1.0)                
##  jsonlite       1.7.2      2020-12-09 [1] CRAN (R 4.1.0)                
##  kableExtra   * 1.3.4      2021-02-20 [1] CRAN (R 4.1.0)                
##  knitr        * 1.33       2021-04-24 [1] CRAN (R 4.1.0)                
##  labeling       0.4.2      2020-10-20 [1] CRAN (R 4.1.0)                
##  labelled       2.8.0      2021-03-08 [1] CRAN (R 4.1.0)                
##  lattice        0.20-44    2021-05-02 [1] CRAN (R 4.1.0)                
##  lifecycle      1.0.0      2021-02-15 [1] CRAN (R 4.1.0)                
##  lme4           1.1-27.1   2021-06-22 [1] CRAN (R 4.1.0)                
##  lubridate      1.7.10     2021-02-26 [1] CRAN (R 4.1.0)                
##  magrittr       2.0.1      2020-11-17 [1] CRAN (R 4.1.0)                
##  MASS         * 7.3-54     2021-05-03 [1] CRAN (R 4.1.0)                
##  Matrix         1.3-4      2021-06-01 [1] CRAN (R 4.1.0)                
##  MBESS          4.8.0      2020-08-05 [1] CRAN (R 4.1.0)                
##  memoise        2.0.0      2021-01-26 [1] CRAN (R 4.1.0)                
##  mgcv           1.8-36     2021-06-01 [1] CRAN (R 4.1.0)                
##  minqa          1.2.4      2014-10-09 [1] CRAN (R 4.1.0)                
##  mnormt         2.0.2      2020-09-01 [1] CRAN (R 4.1.0)                
##  modelr         0.1.8      2020-05-19 [1] CRAN (R 4.1.0)                
##  MOTE         * 1.0.2      2019-04-10 [1] CRAN (R 4.1.0)                
##  munsell        0.5.0      2018-06-12 [1] CRAN (R 4.1.0)                
##  mvtnorm        1.1-2      2021-06-07 [1] CRAN (R 4.1.0)                
##  nlme           3.1-152    2021-02-04 [1] CRAN (R 4.1.0)                
##  nloptr         1.2.2.2    2020-07-02 [1] CRAN (R 4.1.0)                
##  NLP          * 0.2-1      2020-10-14 [1] CRAN (R 4.1.0)                
##  openxlsx       4.2.4      2021-06-16 [1] CRAN (R 4.1.0)                
##  papaja       * 0.1.0.9997 2021-06-11 [1] Github (crsh/papaja@a231c36)  
##  pillar         1.6.1      2021-05-16 [1] CRAN (R 4.1.0)                
##  pkgbuild       1.2.0      2020-12-15 [1] CRAN (R 4.1.0)                
##  pkgconfig      2.0.3      2019-09-22 [1] CRAN (R 4.1.0)                
##  pkgload        1.2.1      2021-04-06 [1] CRAN (R 4.1.0)                
##  plyr           1.8.6      2020-03-03 [1] CRAN (R 4.1.0)                
##  prettyunits    1.1.1      2020-01-24 [1] CRAN (R 4.1.0)                
##  processx       3.5.2      2021-04-30 [1] CRAN (R 4.1.0)                
##  ps             1.6.0      2021-02-28 [1] CRAN (R 4.1.0)                
##  psy          * 1.1        2012-06-21 [1] CRAN (R 4.1.0)                
##  psych        * 2.1.6      2021-06-18 [1] CRAN (R 4.1.0)                
##  purrr        * 0.3.4      2020-04-17 [1] CRAN (R 4.1.0)                
##  qualtRics    * 3.1.4      2021-01-14 [1] CRAN (R 4.1.0)                
##  R6             2.5.0      2020-10-28 [1] CRAN (R 4.1.0)                
##  RColorBrewer * 1.1-2      2014-12-07 [1] CRAN (R 4.1.0)                
##  Rcpp         * 1.0.6      2021-01-15 [1] CRAN (R 4.1.0)                
##  RCurl        * 1.98-1.3   2021-03-16 [1] CRAN (R 4.1.0)                
##  readr        * 1.4.0      2020-10-05 [1] CRAN (R 4.1.0)                
##  readxl         1.3.1      2019-03-13 [1] CRAN (R 4.1.0)                
##  remotes        2.4.0      2021-06-02 [1] CRAN (R 4.1.0)                
##  reprex         2.0.0      2021-04-02 [1] CRAN (R 4.1.0)                
##  reshape        0.8.8      2018-10-23 [1] CRAN (R 4.1.0)                
##  reshape2       1.4.4      2020-04-09 [1] CRAN (R 4.1.0)                
##  rio            0.5.27     2021-06-21 [1] CRAN (R 4.1.0)                
##  rlang        * 0.4.11     2021-04-30 [1] CRAN (R 4.1.0)                
##  rmarkdown      2.9        2021-06-15 [1] CRAN (R 4.1.0)                
##  rprojroot      2.0.2      2020-11-15 [1] CRAN (R 4.1.0)                
##  rstudioapi     0.13       2020-11-12 [1] CRAN (R 4.1.0)                
##  rvest          1.0.0      2021-03-09 [1] CRAN (R 4.1.0)                
##  sass           0.4.0      2021-05-12 [1] CRAN (R 4.1.0)                
##  scales         1.1.1      2020-05-11 [1] CRAN (R 4.1.0)                
##  sessioninfo    1.1.1      2018-11-05 [1] CRAN (R 4.1.0)                
##  sjlabelled     1.1.8      2021-05-11 [1] CRAN (R 4.1.0)                
##  slam           0.1-48     2020-12-03 [1] CRAN (R 4.1.0)                
##  SnowballC    * 0.7.0      2020-04-01 [1] CRAN (R 4.1.0)                
##  stringi        1.6.2      2021-05-17 [1] CRAN (R 4.1.0)                
##  stringr      * 1.4.0      2019-02-10 [1] CRAN (R 4.1.0)                
##  svglite        2.0.0      2021-02-20 [1] CRAN (R 4.1.0)                
##  systemfonts    1.0.2      2021-05-11 [1] CRAN (R 4.1.0)                
##  testthat       3.0.3      2021-06-16 [1] CRAN (R 4.1.0)                
##  tibble       * 3.1.2      2021-05-16 [1] CRAN (R 4.1.0)                
##  tidyr        * 1.1.3      2021-03-03 [1] CRAN (R 4.1.0)                
##  tidyselect     1.1.1      2021-04-30 [1] CRAN (R 4.1.0)                
##  tidyverse    * 1.3.1      2021-04-15 [1] CRAN (R 4.1.0)                
##  tm           * 0.7-8      2020-11-18 [1] CRAN (R 4.1.0)                
##  tmvnsim        1.0-2      2016-12-15 [1] CRAN (R 4.1.0)                
##  TOSTER       * 0.3.4      2018-08-03 [1] CRAN (R 4.1.0)                
##  usethis        2.0.1      2021-02-10 [1] CRAN (R 4.1.0)                
##  utf8           1.2.1      2021-03-12 [1] CRAN (R 4.1.0)                
##  vctrs          0.3.8      2021-04-29 [1] CRAN (R 4.1.0)                
##  viridisLite    0.4.0      2021-04-13 [1] CRAN (R 4.1.0)                
##  webshot        0.5.2      2019-11-22 [1] CRAN (R 4.1.0)                
##  withr          2.4.2      2021-04-18 [1] CRAN (R 4.1.0)                
##  wordcloud    * 2.6        2018-08-24 [1] CRAN (R 4.1.0)                
##  xfun           0.24       2021-06-15 [1] CRAN (R 4.1.0)                
##  XML          * 3.99-0.6   2021-03-16 [1] CRAN (R 4.1.0)                
##  xml2           1.3.2      2020-04-23 [1] CRAN (R 4.1.0)                
##  xtable         1.8-4      2019-04-21 [1] CRAN (R 4.1.0)                
##  yaml           2.2.1      2020-02-01 [1] CRAN (R 4.1.0)                
##  zip            2.2.0      2021-05-31 [1] CRAN (R 4.1.0)                
## 
## [1] /Library/Frameworks/R.framework/Versions/4.1/Resources/library

6.2 Codebook

6.2.1 Metadata

6.2.1.1 Description

Dataset name: results

The dataset has N=846 rows and 34 columns. 0 rows have no missing values on any column.

Metadata for search engines

• Date published: 2021-06-29

x DYUK STUK UKNATION RESIDENT GENDER condition conditionbi genderbi age_cat NATIONALITY INTEREST ATT1 ATT2 ATT3 INTENT1 INTENT2 INTENT3 EXPECT1 EXPECT2 EXPECT3 PERCEPTNUM PERCEPTSCALE CONSTRUAL_1 PERCEPTCHANGE PRECONFORMITY POLITICS AGE attitude_mean intention_mean expect_mean expintent_avg age_cent PERCEPTCHANGE_r comb_future

6.3 Codebook table

name	data_type	ordered	value_labels	n_missing	complete_rate	n_unique	empty	top_counts	min	median	max	mean	sd	whitespace	hist	DYUK	STUK	NATIONALITY	label
<a href=“#DYUK”>DYUK</a>	character	NA	NA	570	0.3262411	276	0	NA	7	NA	898	NA	NA	0	NA	Dynamic UK	NA	NA	NA
<a href=“#STUK”>STUK</a>	character	NA	NA	562	0.3356974	282	0	NA	3	NA	993	NA	NA	0	NA	NA	Static UK	NA	NA
<a href=“#UKNATION”>UKNATION</a>	factor	FALSE	1,<br>2. 2,<br>3. 3,<br>4. 4	24	0.9716312	4	NA	1: 693, 3: 76, 2: 42, 4: 11	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
<a href=“#RESIDENT”>RESIDENT</a>	factor	FALSE	1,<br>2. 2	0	1.0000000	2	NA	1: 839, 2: 7	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
<a href=“#GENDER”>GENDER</a>	factor	FALSE	Male,<br>2. Female,<br>3. Other	0	1.0000000	3	NA	Fem: 477, Mal: 366, Oth: 3	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
<a href=“#condition”>condition</a>	factor	FALSE	Dynamic,<br>2. Static,<br>3. No norm	0	1.0000000	3	NA	No : 286, Sta: 284, Dyn: 276	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
<a href=“#conditionbi”>conditionbi</a>	factor	FALSE	Dynamic,<br>2. Static	286	0.6619385	2	NA	Sta: 284, Dyn: 276	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
<a href=“#genderbi”>genderbi</a>	factor	FALSE	Male,<br>2. Female	3	0.9964539	2	NA	Fem: 477, Mal: 366	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
<a href=“#age_cat”>age_cat</a>	factor	FALSE	Young adults,<br>2. Middle-aged adults,<br>3. Old adults	0	1.0000000	3	NA	Mid: 408, Old: 237, You: 201	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
<a href=“#NATIONALITY”>NATIONALITY</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	3	27.0	183	28.1264775	11.9352031	NA	▇▁▁▁▁	NA	NA	Nationality	NA
<a href=“#INTEREST”>INTEREST</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	4.0	7	3.5780142	1.8171931	NA	▇▅▅▃▅	NA	NA	NA	NA
<a href=“#ATT1”>ATT1</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	5.0	7	4.4739953	1.4647446	NA	▂▂▅▇▅	NA	NA	NA	NA
<a href=“#ATT2”>ATT2</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	5.0	7	4.5851064	1.3911693	NA	▂▂▆▇▆	NA	NA	NA	NA
<a href=“#ATT3”>ATT3</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	5.0	7	4.8758865	1.3734456	NA	▂▂▅▇▇	NA	NA	NA	NA
<a href=“#INTENT1”>INTENT1</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	5.0	7	4.1773050	1.8292186	NA	▆▃▃▇▇	NA	NA	NA	NA
<a href=“#INTENT2”>INTENT2</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	5.0	7	4.1501182	1.8072618	NA	▆▃▅▇▆	NA	NA	NA	NA
<a href=“#INTENT3”>INTENT3</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	5.0	7	4.3439716	1.8470845	NA	▆▂▃▇▇	NA	NA	NA	NA
<a href=“#EXPECT1”>EXPECT1</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	4.0	7	3.8959811	1.7864865	NA	▇▃▅▇▅	NA	NA	NA	NA
<a href=“#EXPECT2”>EXPECT2</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	4.0	7	3.8640662	1.7840077	NA	▇▃▅▇▅	NA	NA	NA	NA
<a href=“#EXPECT3”>EXPECT3</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	4.0	7	4.0378251	1.7564049	NA	▆▅▅▇▆	NA	NA	NA	NA
<a href=“#PERCEPTNUM”>PERCEPTNUM</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	3	30.0	85	28.7482270	11.1468611	NA	▂▇▂▁▁	NA	NA	NA	NA
<a href=“#PERCEPTSCALE”>PERCEPTSCALE</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	2.0	5	2.5685579	0.7485873	NA	▁▇▅▂▁	NA	NA	NA	NA
<a href=“#CONSTRUAL_1”>CONSTRUAL_1</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	10.0	21	11.0955083	4.1522252	NA	▂▆▇▇▂	NA	NA	NA	NA
<a href=“#PERCEPTCHANGE”>PERCEPTCHANGE</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	3.0	7	2.8605201	0.8980857	NA	▅▇▂▁▁	NA	NA	NA	NA
<a href=“#PRECONFORMITY”>PRECONFORMITY</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	4.0	7	4.1796690	1.1959945	NA	▂▆▇▇▃	NA	NA	NA	NA
<a href=“#POLITICS”>POLITICS</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	4.0	7	3.4905437	1.2448352	NA	▆▆▇▃▁	NA	NA	NA	NA
<a href=“#AGE”>AGE</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	18	34.5	79	37.2080378	13.5810251	NA	▇▆▅▂▁	NA	NA	NA	NA
<a href=“#attitude_mean”>attitude_mean</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	5.0	7	4.6449961	1.2912197	NA	▁▂▅▇▃	NA	NA	NA	NA
<a href=“#intention_mean”>intention_mean</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	4.7	7	4.2237983	1.7965065	NA	▅▃▃▇▆	NA	NA	NA	NA
<a href=“#expect_mean”>expect_mean</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	4.0	7	3.9326241	1.7506056	NA	▆▅▅▇▅	NA	NA	NA	NA
<a href=“#expintent_avg”>expintent_avg</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	4.5	7	4.0782112	1.7381013	NA	▆▃▅▇▅	NA	NA	NA	NA
<a href=“#age_cent”>age_cent</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	-19	-2.5	42	0.1955638	13.5810251	NA	▇▆▅▂▁	NA	NA	NA	NA
<a href=“#PERCEPTCHANGE_r”>PERCEPTCHANGE_r</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	1	5.0	7	5.1394799	0.8980857	NA	▁▁▂▇▅	NA	NA	NA	NA
<a href=“#comb_future”>comb_future</a>	numeric	NA	NA	0	1.0000000	NA	NA	NA	2	4.5	7	4.6595745	0.8692358	NA	▁▆▇▅▁	NA	NA	NA	NA

JSON-LD metadata

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{
  "name": "results",
  "datePublished": "2021-06-29",
  "description": "The dataset has N=846 rows and 34 columns.\n0 rows have no missing values on any column.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name            |label | n_missing|\n|:---------------|:-----|---------:|\n|DYUK            |NA    |       570|\n|STUK            |NA    |       562|\n|UKNATION        |NA    |        24|\n|RESIDENT        |NA    |         0|\n|GENDER          |NA    |         0|\n|condition       |NA    |         0|\n|conditionbi     |NA    |       286|\n|genderbi        |NA    |         3|\n|age_cat         |NA    |         0|\n|NATIONALITY     |NA    |         0|\n|INTEREST        |NA    |         0|\n|ATT1            |NA    |         0|\n|ATT2            |NA    |         0|\n|ATT3            |NA    |         0|\n|INTENT1         |NA    |         0|\n|INTENT2         |NA    |         0|\n|INTENT3         |NA    |         0|\n|EXPECT1         |NA    |         0|\n|EXPECT2         |NA    |         0|\n|EXPECT3         |NA    |         0|\n|PERCEPTNUM      |NA    |         0|\n|PERCEPTSCALE    |NA    |         0|\n|CONSTRUAL_1     |NA    |         0|\n|PERCEPTCHANGE   |NA    |         0|\n|PRECONFORMITY   |NA    |         0|\n|POLITICS        |NA    |         0|\n|AGE             |NA    |         0|\n|attitude_mean   |NA    |         0|\n|intention_mean  |NA    |         0|\n|expect_mean     |NA    |         0|\n|expintent_avg   |NA    |         0|\n|age_cent        |NA    |         0|\n|PERCEPTCHANGE_r |NA    |         0|\n|comb_future     |NA    |         0|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "keywords": ["DYUK", "STUK", "UKNATION", "RESIDENT", "GENDER", "condition", "conditionbi", "genderbi", "age_cat", "NATIONALITY", "INTEREST", "ATT1", "ATT2", "ATT3", "INTENT1", "INTENT2", "INTENT3", "EXPECT1", "EXPECT2", "EXPECT3", "PERCEPTNUM", "PERCEPTSCALE", "CONSTRUAL_1", "PERCEPTCHANGE", "PRECONFORMITY", "POLITICS", "AGE", "attitude_mean", "intention_mean", "expect_mean", "expintent_avg", "age_cent", "PERCEPTCHANGE_r", "comb_future"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "DYUK",
      "description": "Dynamic UK",
      "@type": "propertyValue"
    },
    {
      "name": "STUK",
      "description": "Static UK",
      "@type": "propertyValue"
    },
    {
      "name": "UKNATION",
      "value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
      "@type": "propertyValue"
    },
    {
      "name": "RESIDENT",
      "value": "1. 1,\n2. 2",
      "@type": "propertyValue"
    },
    {
      "name": "GENDER",
      "value": "1. Male,\n2. Female,\n3. Other",
      "@type": "propertyValue"
    },
    {
      "name": "condition",
      "value": "1. Dynamic,\n2. Static,\n3. No norm",
      "@type": "propertyValue"
    },
    {
      "name": "conditionbi",
      "value": "1. Dynamic,\n2. Static",
      "@type": "propertyValue"
    },
    {
      "name": "genderbi",
      "value": "1. Male,\n2. Female",
      "@type": "propertyValue"
    },
    {
      "name": "age_cat",
      "value": "1. Young adults,\n2. Middle-aged adults,\n3. Old adults",
      "@type": "propertyValue"
    },
    {
      "name": "NATIONALITY",
      "description": "Nationality",
      "@type": "propertyValue"
    },
    {
      "name": "INTEREST",
      "@type": "propertyValue"
    },
    {
      "name": "ATT1",
      "@type": "propertyValue"
    },
    {
      "name": "ATT2",
      "@type": "propertyValue"
    },
    {
      "name": "ATT3",
      "@type": "propertyValue"
    },
    {
      "name": "INTENT1",
      "@type": "propertyValue"
    },
    {
      "name": "INTENT2",
      "@type": "propertyValue"
    },
    {
      "name": "INTENT3",
      "@type": "propertyValue"
    },
    {
      "name": "EXPECT1",
      "@type": "propertyValue"
    },
    {
      "name": "EXPECT2",
      "@type": "propertyValue"
    },
    {
      "name": "EXPECT3",
      "@type": "propertyValue"
    },
    {
      "name": "PERCEPTNUM",
      "@type": "propertyValue"
    },
    {
      "name": "PERCEPTSCALE",
      "@type": "propertyValue"
    },
    {
      "name": "CONSTRUAL_1",
      "@type": "propertyValue"
    },
    {
      "name": "PERCEPTCHANGE",
      "@type": "propertyValue"
    },
    {
      "name": "PRECONFORMITY",
      "@type": "propertyValue"
    },
    {
      "name": "POLITICS",
      "@type": "propertyValue"
    },
    {
      "name": "AGE",
      "@type": "propertyValue"
    },
    {
      "name": "attitude_mean",
      "@type": "propertyValue"
    },
    {
      "name": "intention_mean",
      "@type": "propertyValue"
    },
    {
      "name": "expect_mean",
      "@type": "propertyValue"
    },
    {
      "name": "expintent_avg",
      "@type": "propertyValue"
    },
    {
      "name": "age_cent",
      "@type": "propertyValue"
    },
    {
      "name": "PERCEPTCHANGE_r",
      "@type": "propertyValue"
    },
    {
      "name": "comb_future",
      "@type": "propertyValue"
    }
  ]
}`